Genetic architecture of the white matter connectome of the human brain

White matter tracts form the structural basis of large-scale brain networks. We applied brain-wide tractography to diffusion images from 30,810 adults (U.K. Biobank) and found significant heritability for 90 node-level and 851 edge-level network connectivity measures. Multivariate genome-wide association analyses identified 325 genetic loci, of which 80% had not been previously associated with brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia, and neurons. The multivariate association profiles implicated 31 loci in connectivity between core regions of the left-hemisphere language network. Polygenic scores for psychiatric, neurological, and behavioral traits also showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to variation in the structural connectome of the human brain.

Supplementary Figure 8. Regional contributions to the significant multivariate associations in the node-level connectivity mvGWAS. For each region we calculated its average multivariate association z-score across all independently associated lead SNPs in the genome. This gives an indication of which node-level connectivities tend to be more often associated with genomic loci in general.
Supplementary Figure 9. Contributions of individual edge-level connectivities to the significant multivariate associations in mvGWAS. For each edge we calculated its average multivariate association z-score across all independently associated lead SNPs in the genome. This gives an indication of which edges tend to be more often associated with genomic loci in general. Left panel: brain maps. Right panel: nodes grouped by different anatomical lobes (frontal, prefrontal, parietal, temporal, occipital cortices, and subcortical structures).
Supplementary Figure 10. Numbers of annotated genes at significantly associated genomic loci using three mapping strategies in FUMA. Left panel: annotated genes for significantly associated genomic loci in the multivariate GWAS of node-level connectivity. Right panel: annotated genes for significantly associated genomic loci in the multivariate GWAS of edge-level connectivity.
Supplementary Figure 11. Manhattan plots for genome-wide gene-based association analysis, separately using the node-level connectivity mvGWAS (upper) and edge-level connectivity mvGWAS (lower) results as input. The red line indicates the Bonferroni-corrected significance threshold for gene-based analysis of 20,146 genes (p<0.025/20,146). Figure 12. Partial correlations between normalized polygenic scores for brain-related disorders or behavioral traits. Confounds including sex and age have been removed -see Methods. The diagonal of the matrix shows the distributions of the polygenic scores across the 30,810 individuals.

Supplementary
Supplementary Figure 13. Node-level connectivities showing the strongest associations with polygenic scores for brain-related disorders or behavioral traits. Left panel: Regions with loadings |r|>0.2 in canonical correlation analysis of their connectivities with a given polygenic score. The regional maps were used to define binary masks to query the Neurosynth database of meta-analyzed functional neuroimaging data from thousands of published studies. Right panel: Brain co-activation maps derived from the "decoder" function of Neurosynth, corresponding to the input masks.
Supplementary Figure 14. Correlations between the loadings derived from different canonical correlation analyses between polygenic scores and node-level connectivities. These correlations are calculated over 90 brain regions. They show the extents to which polygenic dispositions to two different disorders/behaviors tend to associate consistently with the same brain regions. The diagonal of the matrix shows the distributions of loadings across regions.  Table 22. Differential expression of genes associated with node-level connectivity in different lifespan stages from BrainSpan brain samples. Supplementary Table 23. Differential expression of genes associated with edge-level connectivity in different lifespan stages from BrainSpan brain samples. Supplementary Table 24. Differential expression of genes associated with node-level connectivity in different cell types in the developing human brain. Supplementary Table 25. Differential expression of genes associated with edge-level connectivity in different cell types in the developing human brain. Supplementary Table 26. Lead SNPs showing significant association with at least one core language network tract connectivity, and genes annotated to these lead SNPs by genomic position, eQTL and chromatin interaction. Supplementary Table 27. Correlations between polygenic scores for brain-related disorder or behavioural traits. Supplementary Table 28. Loadings from the canonical correlation analyses between node-level connectivities and polygenic dispositions to brain-related disorders or behavioural traits. Supplementary Table 29. Loadings from the canonical correlation analyses between node-level connectivities and polygenic dispositions for Alzheimer's disease after excluding the APOE locus. Supplementary Table 30. Functional terms based on meta-analyzed fMRI data, associated with co-activation maps for brain regions where node-level connectivity had loadings |r|>0.2 for polygenic scores of brain-related disorders or behavioural traits. Supplementary Table 31. Correlation coefficients (across 90 regions) between loadings from multivariate associations of node-level connectivities with polygenic dispositions to brain-related disorders or behavioural traits.